Proposal and Evaluation of the Active Course Classification Support System with Exploitation-oriented Learning

Proposal and Evaluation of the Active Course Classification Support System with Exploitation-oriented Learning
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探究式学习主动课程分类支持系统的提出与评估

DOI:
10.1007/978-3-642-29946-9_32
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发表时间:
2012
期刊:
Lecture Notes in Computer Science
影响因子:
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通讯作者:
Kazuteru Miyazaki and Masaaki Ida
Kazuteru Miyazaki and Masaaki Ida
中科院分区:
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文献类型:
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作者:
Seiya Kuroda;Kazuteru Miyazaki and Hiroaki Kobayashi;Kazuteru Miyazaki;Kazuteru Miyazaki and Masaaki Ida

文献摘要

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国家学位和大学评估机构(NIAD-UE)是日本唯一一家可以根据非大学学生的学分积累授予学位的机构。希望获得 NIAD-UE 学位的申请人必须根据每个学科领域的预先确定的标准对其获得的学分进行分类。该标准由多个项目构成。由相关领域的专家组成的小组委员会根据标准中的第一项来判断申请人的课程学分划分是否适当。最近,提出了主动课程分类支持(ACCS)系统来支持小组委员会验证申请人的课程分类。将分类项目编号输入ACCS,ACCS会建议属于该分类项目编号集合的适当课程。然而,确定适当的项目编号仍然存在一些困难。本研究旨在利用机器学习改进由分委会判定的项目编号的确定方法。我们使用面向开发的学习作为改进 ACCS 的学习方法,并提供一个数值示例来展示我们提出的方法的有效性。
The National Institution for Academic Degrees and University Evaluation (NIAD-UE) is an exclusive institution in Japan which can award academic degrees based on the accumulation of academic credits for non-university students. An applicant who wishes to be awarded a degree from NIAD-UE must classify his obtained credits according to pre-determinedcriteriafor each disciplinary field. The criteria are constructed by several items. A sub-committee of experts in the relevant field judges whether the course credit classification by the applicant is appropriate or not paying attention on the first item in the criteria. Recently, the Active Course Classification Support (ACCS) system has been proposed to support the sub-committee for the validation of applicant’s course classification. Entering a classification item number into ACCS, ACCS suggests an appropriate course that belongs to the set of classification item number. However, some difficulties of deciding appropriate item numbers still remain. This study aims to improve the method of determining item numbers, which should be judged by the sub-committee, by using machine learning. We useExploitation-oriented Learningas the learning method for improving ACCS, and present a numerical example to show the effectiveness of our proposed method.